Many attempts have been made to better understand Internet users, often for marketing purposes. However, these attempts often look at evidence such as web page visits, which only provide an ability to infer what is going on within a user's mind. Most attempts have not looked at ways to directly monitor Internet user beliefs. Those that have are plagued by the challenges of collecting and analyzing enormous data sets.
For instance, social influence, or the capacity to affect others' character, development, or behavior, is subjectively analyzed via manual analysis of online content and manual associations of content with user profiles. Some current methods enable small numbers of influential users to be identified; however, the manual nature of these methods prevents them from being scaled into the tens and hundreds of millions. Other solutions use crowdsourcing or curating to partially overcome the scalability issues associated with manual solutions to these large analysis challenges (e.g., KLOUT and KRED).